CyberTraining: CIU: SJSU Data Science for All Seminar Series

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The Nation's research enterprise faces a shortage of data scientists. Expanding the pipeline of data science students, particularly from underrepresented populations, requires educational institutions to increase awareness of data science and inspire a passion for data in students as they begin their academic careers. Currently, few community colleges or undergraduate programs provide training in cyberinfrastructure tools or data science techniques to a broad student population. This project takes a novel approach to augmenting the Nation's data science workforce by training community college and undergraduate students to provide data analytics support to data scientists through a series of "Data Science for All" extracurricular seminars. The seminars require no prior data science knowledge, emphasize transferable skills, and present a feasible path into data science-related research and other careers for students from a broad array of disciplines and from underrepresented groups without extending their time to graduation. By increasing the Nation's data science capabilities and the diversity of its data science research workforce, the project serves the national interest, as stated by NSF's mission: to promote progress of science and advance the prosperity and welfare of the Nation. The goals of this project are to increase undergraduate student awareness of data-driven science and to grow and diversify the population of students trained to perform data wrangling - the data acquisition, transformation, cleaning, and profiling required to prepare data for analysis. According to industry experts, data wrangling is the "heavy lifting" of data science, constituting up to 80% of a data scientist's daily work. Shifting this time-consuming effort to trained data analysts free data scientists to focus more of their time on research. The project achieves its goals through the development and delivery of widely consumable, extracurricular seminars providing interactive training on data science concepts and industry-leading data wrangling tools to undergraduate and community college students. Initial seminar topics, selected in collaboration with the project's advisory board, include Python, Jupyter notebooks, Apache Spark, Tableau, and demystifying artificial intelligence (AI). The seminars' focus on data wrangling also introduces students to data preparation documentation - capturing the data provenance needed for reproducible science. This project's contribution to the Nation's data science workforce is broadened through the free and open distribution of its seminar materials and supplemental resources and its online instructor support community. To encourage adoption at Bay Area community colleges and universities, instructor training is provided through co-instruction and a teaching-the-teacher model. The project contributes to pedagogical research by identifying instructional approaches most effective in teaching data science to a diverse population of undergraduate students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.